3 Critical Processes that Democratize Enterprise Data

Data is the new gold as far as companies are concerned. While most organizations collect vast data, putting it to use is a different challenge. Analytics and business intelligence are often siloed away in a single department. This type of organization creates issues.

To realize the power of data in your business, you must democratize data analysis. What does democratization mean, though? You can choose an excellent storage solution like a Databricks delta lake and connect it to a BI platform.

However, this solution doesn’t guarantee democratization. Simply put, you must offer access to as many employees as possible so that you’re generating insights from every portion of your company.

A business manager naturally has unique, customer-oriented insights that a central data science team might miss. Democratization is a surefire way to boost your competitive edge in the markets. Here’s how you can implement it in your company.

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Install self-service BI


Business intelligence has come a long way over the past decade. BI platforms were once bulky and needed technical expertise to operate. These days, most BI platforms retrieve data and allow users to pose questions in plain English. For instance, a sales manager can choose variables such as customer ID, sales amounts, and locations to create a custom query with a few clicks.

This self-service BI incentivizes employees to think about the business instead of just their job functions. Data also gives them access to other departments’ performance. For instance, sales teams can align with marketing better by viewing marketing-oriented data. They can connect their sales efforts to financial results by viewing revenue and margin trends.

Your receivables department can align with sales by offering collections data. Sales can use these reports to offer credit terms that are better suited to your company, as a result. Thus, data sharing and collaboration boost alignment within your company.

Self-service BI also increases the ease of running ad-hoc reports, reducing the time it takes to generate insights. During sales presentations, your team can accommodate customer questions quickly and present data in real-time. Thus, ad-hoc reports reduce the number of meetings and offer quick insight.

Internal functions such as finance and operations benefit from this approach as well.

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Organize the right way

Organizing a data analysis function is a challenging task in a company. Most companies adopt a centralized model where a single team collects and dispenses data-driven insights to everyone. This approach has its advantages.

A central team of data science professionals can organize and disseminate data better. These people have received intense training in analysis methods, and you’re likely to receive deep insights from them. Data governance improves since the team can enforce strict standards to preserve data context.

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The problem is that a central team can be far removed from a business with multiple product lines and verticals. In such an instance, installing a separate data science team in each vertical makes sense. However, expecting a data science professional to understand customer nuances as a salesperson does is unrealistic.

Therefore, a hybrid approach works best. A central team can ensure analysis occurs according to best practices, while self-service BI truly democratizes data analysis and gives customer insights. You can rely on your data science team to issue analysis prompts and review ad-hoc analyses from the rest of the organization.

This structure also boosts collaboration between data science and business team members, resulting in more significant insights.

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Audit data usage

While democratization is excellent, it can lead to data corruption if left unchecked. For instance, a business team member might draw wrong conclusions and incorporate new practices. The result is adverse business results that could be catastrophic.

A data use audit prevents such occurrences. One example of an audit-based approach is having a data science professional examine analysis assumptions and conclusions. You must also routinely audit your data sources. Changing data formats or types from a source could lead to bad reports and conclusions.

Check in regularly with teams to examine how they’re using data. Often, democratization efforts fail because teams don’t trust data. In such cases, educate and examine the hurdles to greater data adoption throughout your organization.

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Whether it’s workshops or making data a part of your company’s culture, ensure every employee is on the same page regarding expectations. Review the technology you’re using as well. Often, BI tools become outdated due to the way data changes.

For instance, some tools work well with unstructured data (social media feeds, input from customer service reps, etc.), while others work well with structured sources (revenue, margins, financial data, etc.) Therefore, examine the data your company generates the most and plan accordingly.

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Democratization is the way forward

Companies must draw insights from every part of their organization to succeed in modern marketplaces. Democratization helps them achieve this goal. The steps outlined in this article will help you democratize analytics access and draw insights from every resource in your company.

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